AbstractA wireless health system that collects and processes data on human activities can help both users and medical professionals to monitor health status remotely. Therefore it saves tremendous medical resources and costs compared to traditional treatment in which huge amounts of human effort are involved. We present two systems that can correctly classify human daily life activities with little training, and another system to reconstruct human motion trajectories from commercial low cost MEMS inertial measurement units (IMUs) and the Microsoft® Kinect.

A system that reliably classifies daily life activities can contribute to more effective and economical treatments for patients with chronic conditions or undergoing rehabilitative therapy. We propose a universal hybrid decision tree classifier for this purpose. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. Compared to other methods, the experimental result showed high accuracies in classifying human daily live activities.

After we have an accurate classification of human activities, we present a system to further reconstruct motion trajectories using IMUs and the Kinect. The system fuses different motion reconstruction models to give a better tracking result, in which each model is being weighted and transformed to a universal basis. This model is also expandable to accommodate different resources and environments. Experimental results showed a great improvement over past methods only using a single motion reconstruction scheme.

BiographyChieh Chien received his B.Sc. and M.Sc. degrees in electrical engineering from National Taiwan University (NTU), Taiwan in 2006 and University of California, Los Angeles (UCLA), USA, in 2010 respectively. He is now a Ph.D. candidate in electrical engineering, UCLA. His research interests include machine learning on activity classification and motion tracking / reconstruction.